Association between changes in obesity- and lipid-related indices and the risk of cardiovascular disease: a prospective cohort study in Northwest China.

Authors

Jinlong You, Wu Ding, Xue Wang, Jing Tang, Xiuqian Li, Rui Wang, Yuanyuan Li, Yana Bai, Minzhen Wang, Shan Zheng

Published in

Frontiers in endocrinology. Volume 17. Pages 1975120. Epub Oct 09, 2026.

Abstract

Cardiovascular disease (CVD) is a major global health burden. Traditional indicators inadequately capture CVD risk. This study evaluated the association between dynamic changes in novel obesity- and lipid-related indices and CVD incidence.
A prospective cohort study was conducted with 19,315 CVD-free participants from the Jinchang Cohort in Northwest China. Changes in seven indices-WHtR, METS-VF, CVAI, BRI, TyG, TyG-WC, and TyG-BMI-from baseline to follow-up were classified via K-means clustering, and cumulative indices were calculated. Multivariable logistic and restricted cubic spline models assessed associations with incident CVD. Predictive performance was evaluated using AUC, NRI, and IDI.
Over a median follow-up of 66 months, 3,200 (16.57%) participants developed CVD. The highest change classes of all indices showed significantly increased CVD risk, with the strongest associations observed for TyG-BMI (OR: 3.988, 95% CI: 3.419-4.650), TyG-WC (OR: 3.797, 95% CI: 3.215-4.484), and CVAI (OR: 3.695, 95% CI: 3.085-4.426). A significant dose-response relationship was observed for cumulative indices. Changes in CVAI (AUC: 0.642) and cumulative METS-VF (AUC: 0.659) demonstrated the highest predictive ability. Adding these indices to a model of traditional risk factors significantly improved the AUC, NRI, and IDI for CVD prediction (all p < 0.001). Subgroup analyses were consistent.
Longitudinal increases in obesity- and lipid-related indices may provide modest incremental value for CVD risk stratification when added to traditional risk factors, but their discriminative ability is moderate and further validation is required before clinical application.

PMID:
42920460
Bibliographic data and abstract were imported from PubMed on 24 Oct 2026.

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